A model registry is a crucial component in the machine learning lifecycle, serving as a centralized repository for managing and tracking different versions of machine learning models. It allows data scientists and engineers to catalog models along with their metadata, such as performance metrics and training configurations. By maintaining a clear record of model versions, a model registry facilitates collaboration, ensures reproducibility, and simplifies the deployment process. This is particularly important in production environments, where specific model versions need to be deployed for inference. Overall, a model registry enhances the efficiency and reliability of machine learning operations.